1. Field of the Description
The present invention relates, in general, to methods and systems for controlling a robot (such as a service or entertainment robot) for safe and efficient navigation in spaces (or “workspaces”) with one to many human (or other sentient beings), and more particularly, to a classifier or classifying software module for use by a robot controller (or for controlling a robot), when navigating a robot through a crowded workspace. The classifier is designed and adapted specifically to predict whether a human in the workspace will block (which includes a variety of interactions) the robot's travel through the workspace, or, stated differently, the classifier acts to determine intentions of all humans in a workspace or in the vicinity of the robot to facilitate tasks that require knowledge of such intentions. This could include navigation from a present location to a goal location or destination in the workspace in a manner that is responsive to predicted or future behavior of nearby humans or could include preparing for interaction with humans that show an intention to interact with the robot, e.g., for entertainment or other purposes. It could also serve as an advance warning system for a human monitor of the robot's activities.
2. Relevant Background
Today, robots are widely used in a wide variety of environments including public environments where the robots may need to interact with or at least safely navigate through or nearby to humans. Human behavior is complex, which makes it difficult to accurately predict what any particular person will do in the future. This is a primary challenge or difficulty in planning robot interactions with people and for navigating a robot through a space (or “workspace”) that may include a few humans or may even be very crowded with humans.
In more traditional applications, robots are used in industrial environments in confined spaces where humans may have limited access. More recently, though, service robots have been designed and distributed that are configured to interact with humans. Therefore, safe navigation is becoming a more important challenge for such robots, and, as a result, human-aware motion planning has been an increasingly active area of research for those in the robotics industry. One goal of a designer of a motion planner for a robot is to generate robot paths (or trajectories) in the workspace that are physically and psychologically safe for the humans and also for the robot itself.
In many previous planner design approaches, a scenario is assumed in which the robot interacts with one person or only a few people. In the near future (or presently), there will be robots in public spaces such as streets or parks for service and entertainment purposes. In such “crowded” environments, robots will be required to be aware of multiple humans on an ongoing basis in order to navigate safely through the workspace (e.g., the park or street). Several prior works have modelled the more crowded situation by considering humans in the workspace to be dynamic obstacles that the robot needs to avoid (e.g., all small children will abuse the robot so as to block its travel and should be avoided). Other motion planners have instead computed joint motions among the robot and humans using the assumption that both will act in a cooperative manner (as is common in human-to-human interactions in a crowd) to avoid blocking each other's travel. These assumptions may be useful in some settings, such as for a service robot that is assisting in people's daily tasks, but these assumptions may be erroneous in other settings, such as for an entertainment-providing robot, where human behavior toward a robot can be much more difficult to predict.
In one particular example, it may be desirable to provide a robot that wanders around a public space, such as an amusement park, during an event and intentionally interacts with people in a crowd for entertainment purposes. Some of the people will almost certainly approach the robot, as opposed to moving out of its path as in the collaborative model, while others may enjoy the robot from a safe distance (e.g., not block its path or interact in any way). The people who approach the robot may block its present trajectory and may even engage in activities that are potentially harmful to the robot. For example, a recent study has shown that children will sometimes exhibit abusive behavior towards a social robot including persistently obstructing its movement away from the child.
Hence, there is a growing need for a robot controller (or a motion planner for such a controller) that allows the robot to predict blocking (including interactive) behaviors of humans early in order to allow the robot to react appropriately and perform ameliorative actions such as altering the present trajectory or travel path in a work space to avoid a blocking human or move toward such a human for intentional interaction in some cases (e.g., to interact and entertain “interested” or “curious” guests at a theme park).
In this description, interaction is defined as being with “entities” or “tracked entities” in a workspace, and it is intended that such entities would include humans and other sentient beings and could even include other robots. Also, the term “robot” is intended to be construed broadly to include any mobile device that may be navigated through a space with an onboard controller, an offboard controller, and/or with manual instructions from a human operator, and these robots may include service robots, entertainment robots, smart cars, and the like.